Lab: Bioconductor tools for mass spectrometry and proteomics

Laurent Gatto

Abstract In this workflow, we will use R/Bioconductor packages to explore, process, visualise and understand mass spectrometry-based proteomics data, starting with raw data, and proceeding with identification and quantitation data, discussing some of their peculiarities compared to sequencing data along the way. The workflow is aimed at a beginner to intermediate level, such as, for example, seasoned R users who want to get started with mass spectrometry and proteomics, or proteomics practitioners who want to familiarise themselves with R and Bioconductor infrastructure.

This material available under a creative common CC-BY license. You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially.

1 Introduction

Before we start:

If you identify typos, if there are parts that you would like to see expended or clarified, please let me know by telling me directly (during workshops), opening a github issue or by emailing me directly. Please do also briefly specify your background/familiarity with mass spectrometry and/or proteomics (beginner, intermediate or expert) so that I can update accordingly.

In recent years, there we have seen an increase in the number of packages to analyse mass spectrometry and proteomics data for R and Bioconductor, as well as an increase in total number of downloads. See vignette Proteomics packages in Bioconductor for more details and code underlying these figures.

It is also good to highlight that several of these package have become a group efforts, supported by several developers in the community. This post illustrates the various contributions to MSnbase. mzR has benefited by a similar wide range of successful contributions. Both packages, and in particular mzR, are used by many others, and will be described in some detail in this workflow.

This workflow illustrates R / Bioconductor infrastructure for proteomics. Topics covered focus on support for open community-driven formats for raw data and identification results, packages for peptide-spectrum matching, data processing and analysis:

• Exploring available infrastructure
• Mass spectrometry data
• Getting data from proteomics repositories
• Handling raw MS data
• Handling identification data
• MS/MS database search
• Analysing search results
• High-level data interface
• Quantitative proteomics
• Importing third-party quantitation data
• Data processing and analysis
• Statistical analysis
• Machine learning
• Annotation
• Other relevant packages/pipelines

Links to other packages and references are also documented. In particular, the vignettes included in the RforProteomics package also contains relevant material.

Other material

This workflow provides a general introduction to Bioconductor software for mass spectrometry and proteomics. If you are interested in

• The application of machine learning to proteomics data, in particular spatial proteomics (i.e. the sub-cellular localisation), follow the tutorial vignette from pRoloc package, accessible with vignette("pRoloc-tutorial", package = "pRoloc") or online.
• The analysis of identification data to retain the most reliable PSMs, see the MSnID vignette11 Section Analysing search results below is a summary of that vignette., accessible with vignette("msnid_vignette", package = "MSnID") or online. In addition, the vignettes of the msmsTest package describe how to analyse spectral counting data using packages dedicated for the analysis of high throughput sequencing data.
• The analysis of MS^E data independent acquisition (DIA), see the vignettes in the synapter package.
• The processing and analysis of MALDI-MS data, read the MALDIquant introduction accessible with vignette("MALDIquant-intro", package = "MALDIquant") and available online.
• The processing and analysis of imaging mass spectrometry (IMS), read the Carinal walkthrough vignette accessible with vignette("Cardinal-walkthrough", package = "Cardinal") and online.
• …

Setup

The follow packages will be used throughout this documents. R version 3.3.1 or higher is required to install all the packages using BiocInstaller::biocLite.

library("mzR")
library("mzID")
library("MSnID")
library("MSnbase")
library("rpx")
library("MLInterfaces")
library("pRoloc")
library("pRolocdata")
library("MSGFplus")
library("rols")
library("hpar")

The most convenient way to install all the tutorials requirement (and more related content), is to install RforProteomics with all its dependencies.

library("BiocInstaller")
biocLite("RforProteomics", dependencies = TRUE)

Other packages of interest, such as rTANDEM or MSGFgui will be described later in the document but are not required to execute the code in this workflow.

Exploring available infrastructure

In Bioconductor version 3.6, there are respectively 92 proteomics, 62 mass spectrometry software packages and 17 mass spectrometry experiment packages. These respective packages can be extracted with the proteomicsPackages(), massSpectrometryPackages() and massSpectrometryDataPackages() and explored interactively, or looked at by exploring the respective biocViews on the Bioconductor web page.

library("RforProteomics")
pp <- proteomicsPackages()
display(pp)

Mass spectrometry data

Most community-driven formats described in the table are supported in R. We will see how to read and access these data in the following sections.

## Warning: package 'knitr' was built under R version 3.6.0

Type	Format	Package
raw	mzML, mzXML, netCDF, mzData	MSnbase (read and write in version >= 2.3.13) via mzR
identification	mzIdentML	mzID (read) and MSnbase (read, via mzR)
quantitation	mzQuantML
peak lists	mgf	MSnbase (read)
other	mzTab	MSnbase (read)

2 How does mass spectrometry work?

Mass spectrometry (MS) is a technology that separates charged molecules (ions) based on their mass to charge ratio (M/Z). It is often coupled to chromatography (liquid LC, but can also be gas-based GC). The time an analytes takes to elute from the chromatography column is the retention time.

A chromatogram, illustrating the total amount of analytes over the retention time.

An mass spectrometer is composed of three components:

1. The source, that ionises the molecules: examples are Matrix-assisted laser desorption/ionisation (MALDI) or electrospray ionisation. (ESI)
2. The analyser, that separates the ions: Time of flight (TOF) or Orbitrap.
3. The detector that quantifies the ions.

When using mass spectrometry for proteomics, the proteins are first digested with a protease such as trypsin. In mass shotgun proteomics, the analytes assayed in the mass spectrometer are peptides.

Often, ions are subjected to more than a single MS round. After a first round of separation, the peaks in the spectra, called MS1 spectra, represent peptides. At this stage, the only information we possess about these peptides are their retention time and their mass-to-charge (we can also infer their charge be inspecting their isotopic envelope, i.e the peaks of the individual isotopes, see below), which is not enough to infer their identify (i.e. their sequence).

In MSMS (or MS2), the settings of the mass spectrometer are set automatically to select a certain number of MS1 peaks (for example 20). Once a narrow M/Z range has been selected (corresponding to one high-intensity peak, a peptide, and some background noise), it is fragmented (using for example collision-induced dissociation (CID), higher energy collisional dissociation (HCD) or electron-transfer dissociation (ETD)). The fragment ions are then themselves separated in the analyser to produce a MS2 spectrum. The unique fragment ion pattern can then be used to infer the peptide sequence using de novo sequencing (when the spectrum is of high enough quality) of using a search engine such as, for example Mascot, MSGF+, …, that will match the observed, experimental spectrum to theoratical spectra (see details below).

Schematics of a mass spectrometer and two rounds of MS.

The animation below show how 25 ions different ions (i.e. having different M/Z values) are separated throughout the MS analysis and are eventually detected (i.e. quantified). The final frame shows the hypothetical spectrum.

Separation and detection of ions in a mass spectrometer.

The figures below illustrate the two rounds of MS. The spectrum on the left is an MS1 spectrum acquired after 21 minutes and 3 seconds of elution. 10 peaks, highlited by dotted vertical lines, were selected for MS2 analysis. The peak at M/Z 460.79 (488.8) is highlighted by a red (orange) vertical line on the MS1 spectrum and the fragment spectra are shown on the MS2 spectrum on the top (bottom) right figure.

Parent ions in the MS1 spectrum (left) and two sected fragment ions MS2 spectra (right).

The figures below represent the 3 dimensions of MS data: a set of spectra (M/Z and intensity) of retention time, as well as the interleaved nature of MS1 and MS2 (and there could be more levels) data.

MS1 spectra over retention time.

MS2 spectra interleaved between two MS1 spectra.

3 Accessing data

3.1 From the ProteomeXchange database

MS-based proteomics data is disseminated through the ProteomeXchange infrastructure, which centrally coordinates submission, storage and dissemination through multiple data repositories, such as the PRoteomics IDEntifications (PRIDE) database at the EBI for mass spectrometry-based experiments (including quantitative data, as opposed as the name suggests), PASSEL at the ISB for Selected Reaction Monitoring (SRM, i.e. targeted) data and the MassIVE resource. These data can be downloaded within R using the rpx package.

library("rpx")
pxannounced()

## 15 new ProteomeXchange annoucements

##     Data.Set    Publication.Data             Message
## 1  PXD007122 2018-07-10 09:35:52                 New
## 2  PXD010346 2018-07-10 04:09:06                 New
## 3  PXD010345 2018-07-09 16:41:16                 New
## 4  PXD010344 2018-07-09 16:41:06                 New
## 5  PXD009495 2018-07-09 07:22:36                 New
## 6  PXD009826 2018-07-06 14:39:06                 New
## 7  PXD009939 2018-07-06 12:39:44                 New
## 8  PXD007761 2018-07-06 12:38:09 Updated information
## 9  PXD005923 2018-07-06 10:00:35 Updated information
## 10 PXD008069 2018-07-06 07:27:24                 New
## 11 PXD005973 2018-07-05 17:28:53                 New
## 12 PXD008592 2018-07-05 17:25:40                 New
## 13 PXD008242 2018-07-05 17:23:04                 New
## 14 PXD007573 2018-07-05 13:57:46                 New
## 15 PXD007601 2018-07-05 13:54:08                 New

Using the unique PXD000001 identifier, we can retrieve the relevant metadata that will be stored in a PXDataset object. The names of the files available in this data can be retrieved with the pxfiles accessor function.

px <- PXDataset("PXD000001")
px

## Object of class "PXDataset"
##  Id: PXD000001 with 12 files
##  [1] 'F063721.dat' ... [12] 'generated'
##  Use 'pxfiles(.)' to see all files.

pxfiles(px)

##  [1] "F063721.dat"                                                         
##  [2] "F063721.dat-mztab.txt"                                               
##  [3] "PRIDE_Exp_Complete_Ac_22134.xml.gz"                                  
##  [4] "PRIDE_Exp_mzData_Ac_22134.xml.gz"                                    
##  [5] "PXD000001_mztab.txt"                                                 
##  [6] "README.txt"                                                          
##  [7] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML" 
##  [8] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzXML"
##  [9] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML"         
## [10] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw"           
## [11] "erwinia_carotovora.fasta"                                            
## [12] "generated"

Other metadata for the px data set:

pxtax(px)

## [1] "Erwinia carotovora"

pxurl(px)

## [1] "ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001"

pxref(px)

## [1] "Gatto L, Christoforou A. Using R and Bioconductor for proteomics data analysis. Biochim Biophys Acta. 2014 1844(1 pt a):42-51"

Data files can then be downloaded with the pxget function. Below, we retrieve the raw data file. The file is downloaded22 If the file is already available, it is not downloaded a second time. in the working directory and the name of the file is return by the function and stored in the mzf variable for later use.

fn <- "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML"
mzf <- pxget(px, fn)

## Downloading 1 file

## /home/lg390/Documents/Teaching/bioc-ms-prot/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML already present.

mzf

## [1] "/home/lg390/Documents/Teaching/bioc-ms-prot/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML"

3.2 From AnnotationHub

AnnotationHub is a cloud resource set up and managed by the Bioconductor project that serves various omics datasets. It is possible to contribute and access (albeit currently only a limited number of) proteomics data.

library("AnnotationHub")

## 
## Attaching package: 'AnnotationHub'

## The following object is masked from 'package:Biobase':
## 
##     cache

ah <- AnnotationHub()

## snapshotDate(): 2018-04-30

query(ah, "proteomics")

## AnnotationHub with 4 records
## # snapshotDate(): 2018-04-30 
## # $dataprovider: PRIDE
## # $species: Erwinia carotovora
## # $rdataclass: AAStringSet, MSnSet, mzRident, mzRpwiz
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## #   tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH49006"]]' 
## 
##             title                                                         
##   AH49006 | PXD000001: Erwinia carotovora and spiked-in protein fasta file
##   AH49007 | PXD000001: Peptide-level quantitation data                    
##   AH49008 | PXD000001: raw mass spectrometry data                         
##   AH49009 | PXD000001: MS-GF+ identiciation data

ms <- ah[["AH49008"]]
ms

## Mass Spectrometry file handle.
## Filename:  55314 
## Number of scans:  7534

The data contains 7534 spectra - 1431 MS1 spectra and 6103 MS2 spectra. The file name, 55314, is not very descriptive because the data originates from the AnnotationHub cloud repository. If the data was read from a local file, is would be named as the mzML (or mzXML) file (see below).

3.3 Data packages

Some data are also distributed through dedicated packages. The msdata, for example, provides some general raw data files relevant for both proteomics and metabolomics.

library("msdata")
## proteomics raw data
proteomics()

## [1] "MRM-standmix-5.mzML.gz"                                                
## [2] "MS3TMT10_01022016_32917-33481.mzML.gz"                                 
## [3] "MS3TMT11.mzML"                                                         
## [4] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML.gz"
## [5] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz"

## proteomics identification data
ident()

## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"

More often, such experiment packages distribute processed data; an example of such is the pRolocdata package, that offers quantitative proteomics data.

4 Handling raw MS data

4.1 High-level data structures

The MSnbase package provides high-level data abstractions for raw MS data through the MSnExp class and containers for quantification data via the MSnSet class (see below). Both store

1. the actual assay data (spectra or quantitation matrix, see below), accessed with spectra (or the [, [[ operators) or exprs;
2. sample metadata, accessed as a data.frame with pData;
3. feature metadata, accessed as a data.frame with fData.

Another useful slot is processingData, accessed with processingData(.), that records all the processing that objects have undergone since their creation (see examples below).

4.2 Raw data: the MSnExp class

The readMSData will parse the raw data and construct an MS experiment object of class MSnExp. An important argument to readMSData is the mode, which can be "onDisk" or "inMemory". The former doesn’t load the raw data in memory (which is not advised for MS1 data, or when many files are loaded) and is generally the recommended mode. See the benchmarking vignette[^Open it with vignette("benchmarking", package = "MSnbase") or read it online] for details).

library("MSnbase")
## get a small test data
rawFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
               full.name = TRUE, pattern = "mzXML$")
basename(rawFile)

## [1] "dummyiTRAQ.mzXML"

msexp <- readMSData(rawFile, msLevel = 2L)
msexp

## MSn experiment data ("MSnExp")
## Object size in memory: 0.18 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of spectra: 5 
##  MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Tue Jul 10 15:18:02 2018 
##  MSnbase version: 2.6.1 
## - - - Meta data  - - -
## phenoData
##   rowNames: dummyiTRAQ.mzXML
##   varLabels: sampleNames
##   varMetadata: labelDescription
## Loaded from:
##   dummyiTRAQ.mzXML 
## protocolData: none
## featureData
##   featureNames: F1.S1 F1.S2 ... F1.S5 (5 total)
##   fvarLabels: spectrum
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'

Spectra can be extracted as a list of Spectrum2 objects with the spectra accessor or as a subset of the original MSnExp data with the [ operator. Individual spectra can be accessed with [[.

length(msexp)

## [1] 5

msexp[1:2]

## MSn experiment data ("MSnExp")
## Object size in memory: 0.07 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of spectra: 2 
##  MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Tue Jul 10 15:18:02 2018 
## Data [numerically] subsetted 2 spectra: Tue Jul 10 15:18:02 2018 
##  MSnbase version: 2.6.1 
## - - - Meta data  - - -
## phenoData
##   rowNames: dummyiTRAQ.mzXML
##   varLabels: sampleNames
##   varMetadata: labelDescription
## Loaded from:
##   dummyiTRAQ.mzXML 
## protocolData: none
## featureData
##   featureNames: F1.S1 F1.S2
##   fvarLabels: spectrum
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'

msexp[[2]]

## Object of class "Spectrum2"
##  Precursor: 546.9586 
##  Retention time: 25:2 
##  Charge: 3 
##  MSn level: 2 
##  Peaks count: 1012 
##  Total ion count: 56758067

The identification results stemming from the same raw data file can then be used to add PSM matches.

## initial feature variable
fData(msexp)

##       spectrum
## F1.S1        1
## F1.S2        2
## F1.S3        3
## F1.S4        4
## F1.S5        5

## find path to a mzIdentML file
identFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
                 full.name = TRUE, pattern = "dummyiTRAQ.mzid")
basename(identFile)

## [1] "dummyiTRAQ.mzid"

msexp <- addIdentificationData(msexp, identFile)
## additional feature variables
fvarLabels(msexp)

##  [1] "spectrum"                 "acquisition.number"      
##  [3] "sequence"                 "chargeState"             
##  [5] "rank"                     "passThreshold"           
##  [7] "experimentalMassToCharge" "calculatedMassToCharge"  
##  [9] "modNum"                   "isDecoy"                 
## [11] "post"                     "pre"                     
## [13] "start"                    "end"                     
## [15] "DatabaseAccess"           "DBseqLength"             
## [17] "DatabaseSeq"              "DatabaseDescription"     
## [19] "idFile"                   "MS.GF.RawScore"          
## [21] "MS.GF.DeNovoScore"        "MS.GF.SpecEValue"        
## [23] "MS.GF.EValue"             "modName"                 
## [25] "modMass"                  "modLocation"             
## [27] "subOriginalResidue"       "subReplacementResidue"   
## [29] "subLocation"              "nprot"                   
## [31] "npep.prot"                "npsm.prot"               
## [33] "npsm.pep"

We see that 3 out of 5 MS2 spectra in the msexp data have been identified; those that haven’t have missing values for the new, id-related feature variables.

fData(msexp)$rank

## [1]  1  1 NA NA  1

fData(msexp)$isDecoy

## [1] FALSE FALSE    NA    NA FALSE

Exercise Load all MS level data from file MS3TMT11.mzML available in the msdata package using readMSData, making sure you set mode = "onDisk", and verify which MS levels (accessible with the msLevel function) are centroided (accessible with the centroided() function). See section Raw data processing for data in profile and centroided (processed) modes.

f <- proteomics(full.names = TRUE, pattern = "MS3TMT11.mzML")
ms <- readMSData(f, mode = "onDisk")
table(centroided(ms), msLevel(ms))

##        
##           1   2   3
##   FALSE  30   0   0
##   TRUE    0 482 482

Spectra and (parts of) experiments can be extracted and plotted.

msexp[[1]]

## Object of class "Spectrum2"
##  Precursor: 645.3741 
##  Retention time: 25:1 
##  Charge: 3 
##  MSn level: 2 
##  Peaks count: 2921 
##  Total ion count: 668170086

plot(msexp[[1]], full=TRUE)

Plotting a object of class Spectrum.

As this data was labeled with iTRAQ4 isobaric tags, we can highlight these four peaks of interest with

plot(msexp[[1]], full=TRUE, reporters = iTRAQ4)

Plotting a object of class Spectrum with reporter ions.

msexp[1:3]

## MSn experiment data ("MSnExp")
## Object size in memory: 0.11 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of spectra: 3 
##  MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Tue Jul 10 15:18:02 2018 
## Data [numerically] subsetted 3 spectra: Tue Jul 10 15:18:05 2018 
##  MSnbase version: 2.6.1 
## - - - Meta data  - - -
## phenoData
##   rowNames: dummyiTRAQ.mzXML
##   varLabels: sampleNames
##   varMetadata: labelDescription
## Loaded from:
##   dummyiTRAQ.mzXML 
## protocolData: none
## featureData
##   featureNames: F1.S1 F1.S2 F1.S3
##   fvarLabels: spectrum acquisition.number ... npsm.pep (33 total)
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'

plot(msexp[1:3], full=TRUE)

Plotting a object of class MSnExp

In the examples ablove, we only used a single file as input to readMSData, but multiple file can be read into a single MSnExp object. The origin of the spectra can be accessed with the fromFile function:

fromFile(msexp)

## F1.S1 F1.S2 F1.S3 F1.S4 F1.S5 
##     1     1     1     1     1

Exercise Repeat the previous combination of raw and identification data with the TMT_Erwinia TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML.gz and TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid files from msdata. Retain only MS level data; this can be done either when reading the data in (see the msLevel argument in ?readMSData) or can be done afterwards by filtering the MS levels with filterMsLevel.

## read raw data
rwf <- proteomics(pattern = "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML.gz", full.names = TRUE)
tmterw <- readMSData(rwf, mode = "onDisk")
## or, only read MS2-leve data
## tmterw <- readMSData(rwf, mode = "onDisk", msLevel = 2L)

## add identification data
idf <- ident(full.names = TRUE)

## Error: Character input expected

tmterw <- addIdentificationData(tmterw, idf)

## Error in addIdentificationData(tmterw, idf): object 'idf' not found

tmterw2 <- filterMsLevel(tmterw, 2L)

## It is also possible to chain operations
library("magrittr")
tmterw2 <- rwf %>%
    readMSData(mode = "onDisk") %>%
    addIdentificationData(idf) %>%
    filterMsLevel(2L)

Exercise Still using the TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210 data from the previous exercise, identify the index of the MS2 spectrum with the highest precursor intensity (see the precursorIntensity feature variable) and plot it as illustrated above.

i <- which.max(precursorIntensity(tmterw2))
sp <- tmterw2[[i]]
plot(sp, full = TRUE)

4.3 Low-level access to raw data

This section illustrates the underlying infrastructure from the mzR package, that is used by MSnbase under the hood. It is recommended to use the high level interfaces, as it supports multiple files and does data integrity checks throughout the data processing.

The mzR package provides an interface to the proteowizard C/C++ code base to access various raw data files, such as mzML, mzXML, netCDF, and mzData. The data is accessed on-disk, i.e it is not loaded entirely in memory, and only when explicitly requested. The three main functions are openMSfile to create a file handle to a raw data file, header to extract metadata about the spectra contained in the file and peaks to extract one or multiple spectra of interest. Other functions such as instrumentInfo, or runInfo can be used to gather general information about a run.

Below, we access the raw data file downloaded in the previous section and open a file handle that will allow us to extract data and metadata of interest.

library("mzR")
basename(mzf)

## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML"

ms <- openMSfile(mzf)
ms

## Mass Spectrometry file handle.
## Filename:  TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML 
## Number of scans:  7534

The object loaded from AnnotationHub in the previous section is of the same type, and was also created by the openMSfile function. All operations below can equally be applied to it.

The header function returns the metadata of all available peaks:

hd <- header(ms)
dim(hd)

## [1] 7534   25

names(hd)

##  [1] "seqNum"                   "acquisitionNum"          
##  [3] "msLevel"                  "polarity"                
##  [5] "peaksCount"               "totIonCurrent"           
##  [7] "retentionTime"            "basePeakMZ"              
##  [9] "basePeakIntensity"        "collisionEnergy"         
## [11] "ionisationEnergy"         "lowMZ"                   
## [13] "highMZ"                   "precursorScanNum"        
## [15] "precursorMZ"              "precursorCharge"         
## [17] "precursorIntensity"       "mergedScan"              
## [19] "mergedResultScanNum"      "mergedResultStartScanNum"
## [21] "mergedResultEndScanNum"   "injectionTime"           
## [23] "filterString"             "spectrumId"              
## [25] "centroided"

We can extract metadata and scan data for scan 1000 as follows:

hd[1000, ]

##      seqNum acquisitionNum msLevel polarity peaksCount totIonCurrent
## 1000   1000           1000       2        1        274       1048554
##      retentionTime basePeakMZ basePeakIntensity collisionEnergy
## 1000      1106.916    136.061            164464              45
##      ionisationEnergy    lowMZ   highMZ precursorScanNum precursorMZ
## 1000                0 104.5467 1370.758              992    683.0817
##      precursorCharge precursorIntensity mergedScan mergedResultScanNum
## 1000               2           689443.7          0                   0
##      mergedResultStartScanNum mergedResultEndScanNum injectionTime
## 1000                        0                      0      55.21463
##                                                  filterString
## 1000 FTMS + p NSI d Full ms2 683.08@hcd45.00 [100.00-1380.00]
##                                         spectrumId centroided
## 1000 controllerType=0 controllerNumber=1 scan=1000       TRUE

head(peaks(ms, 1000))

##          [,1]     [,2]
## [1,] 104.5467 308.9326
## [2,] 104.5684 308.6961
## [3,] 108.8340 346.7183
## [4,] 109.3928 365.1236
## [5,] 110.0345 616.7905
## [6,] 110.0703 429.1975

plot(peaks(ms, 1000), type = "h", xlab = "M/Z", ylab = "Intensity")

Manual extraction and plotting of an MS spectrum

5 Handling identification data

The RforProteomics package distributes a small identification result file that we load and parse using infrastructure from the mzR package.

idf <- msdata::ident(full.names = TRUE)
basename(idf)

## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"

The easiest way to read identification data in mzIdentML (often abbreviated with mzid) into R is to read it with readMzIdData, that will parse it, process it, and return a data.frame:

iddf <- readMzIdData(idf)
head(iddf)

##                    sequence                                    spectrumID
## 1              RQCRTDFLNYLR controllerType=0 controllerNumber=1 scan=2949
## 2    ESVALADQVTCVDWRNRKATKK controllerType=0 controllerNumber=1 scan=6534
## 3              KELLCLAMQIIR controllerType=0 controllerNumber=1 scan=5674
##   chargeState rank passThreshold experimentalMassToCharge
## 1           3    1          TRUE                 548.2856
## 2           2    1          TRUE                1288.1528
## 3           2    1          TRUE                 744.4109
##   calculatedMassToCharge modNum isDecoy post pre start end DatabaseAccess
## 1               547.9474      1   FALSE    V   R   574 585        ECA2006
## 2              1288.1741      1   FALSE    G   R    69  90        ECA1676
## 3               744.4255      1    TRUE    Q   R   131 142    XXX_ECA2855
##   DBseqLength DatabaseSeq
## 1        1295            
## 2         110            
## 3         157            
##                                          DatabaseDescription
## 1                             ECA2006 ATP-dependent helicase
## 2                 ECA1676 putative growth inhibitory protein
## 3                                                           
##   acquisitionNum
## 1           2949
## 2           6534
## 3           5674
##                                                          spectrumFile
## 1 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML
## 2 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML
## 3 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML
##                                                                idFile
## 1 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid
## 2 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid
## 3 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid
##   MS.GF.RawScore MS.GF.DeNovoScore MS.GF.SpecEValue MS.GF.EValue
## 1             10               101     4.617121e-08    0.1321981
## 2             12               121     7.255875e-08    0.2087481
## 3              8                74     9.341019e-08    0.2674533
##   MS.GF.QValue MS.GF.PepQValue         modName  modMass modLocation
## 1    0.5254237       0.5490196 Carbamidomethyl 57.02146           3
## 2    0.6103896       0.6231884 Carbamidomethyl 57.02146          11
## 3    0.6250000       0.6363636 Carbamidomethyl 57.02146           5
##   subOriginalResidue subReplacementResidue subLocation
## 1               <NA>                  <NA>          NA
## 2               <NA>                  <NA>          NA
## 3               <NA>                  <NA>          NA
##  [ reached getOption("max.print") -- omitted 3 rows ]

When adding identification data with the addIdentificationData function as shown above, the data is read with readMzIdData, as shown above, and is cleaned up:

• only PSMs matching the regular (non-decoy) database are retained;
• PSMs or rank greater than 1 are discarded;
• only proteotypic peptides are kept.

This behaviour can be replicates with the filterIdentificationDataFrame function.

5.1 Low level access

Along the lines of what is available for raw data, the parsing of this XML-based format comes from mzR. A file handle to mzIdentML files can be created with the openIDfile function. As for raw data, the underlying C/C++ code comes from the proteowizard.

library("mzR")
id1 <- openIDfile(idf)
id1

## Identification file handle.
## Filename:  TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid 
## Number of psms:  5759

Various data can be extracted from the identification object. The peptide spectrum matches (PSMs) and the identification scores can be accessed as a data.frame with psms and score respectively.

softwareInfo(id1)

## [1] "MS-GF+ Beta (v10072) "                      
## [2] "ProteoWizard MzIdentML 3.0.501 ProteoWizard"

enzymes(id1)

##      name nTermGain cTermGain minDistance missedCleavages
## 1 Trypsin                               0            1000

fid1 <- mzR::psms(id1)
head(fid1)

##                                      spectrumID chargeState rank
## 1 controllerType=0 controllerNumber=1 scan=5782           3    1
## 2 controllerType=0 controllerNumber=1 scan=6037           3    1
## 3 controllerType=0 controllerNumber=1 scan=5235           3    1
## 4 controllerType=0 controllerNumber=1 scan=5397           3    1
## 5 controllerType=0 controllerNumber=1 scan=6075           3    1
##   passThreshold experimentalMassToCharge calculatedMassToCharge
## 1          TRUE                1080.2325              1080.2321
## 2          TRUE                1002.2089              1002.2115
## 3          TRUE                1189.2836              1189.2800
## 4          TRUE                 960.5365               960.5365
## 5          TRUE                1264.3409              1264.3419
##                              sequence modNum isDecoy post pre start end
## 1 PVQIQAGEDSNVIGALGGAVLGGFLGNTIGGGSGR      0   FALSE    S   R    50  84
## 2        TQVLDGLINANDIEVPVALIDGEIDVLR      0   FALSE    R   K   288 315
## 3   TKGLNVMQNLLTAHPDVQAVFAQNDEMALGALR      0   FALSE    A   R   192 224
## 4         SQILQQAGTSVLSQANQVPQTVLSLLR      0   FALSE    -   R   264 290
## 5 PIIGDNPFVVVLPDVVLDESTADQTQENLALLISR      0   FALSE    F   R   119 153
##   DatabaseAccess DBseqLength DatabaseSeq
## 1        ECA1932         155            
## 2        ECA1147         434            
## 3        ECA0013         295            
## 4        ECA1731         290            
## 5        ECA1443         298            
##                                         DatabaseDescription acquisitionNum
## 1                        ECA1932 outer membrane lipoprotein           5782
## 2                                    ECA1147 trigger factor           6037
## 3                ECA0013 ribose-binding periplasmic protein           5235
## 4                                         ECA1731 flagellin           5397
## 5      ECA1443 UTP--glucose-1-phosphate uridylyltransferase           6075
##  [ reached getOption("max.print") -- omitted 1 row ]

sc1 <- mzR::score(id1)
head(sc1)

##                                      spectrumID MS.GF.RawScore
## 1 controllerType=0 controllerNumber=1 scan=5782            147
## 2 controllerType=0 controllerNumber=1 scan=6037            214
## 3 controllerType=0 controllerNumber=1 scan=5235            211
## 4 controllerType=0 controllerNumber=1 scan=5397            154
## 5 controllerType=0 controllerNumber=1 scan=6075            188
## 6 controllerType=0 controllerNumber=1 scan=5761            123
##   MS.GF.DeNovoScore MS.GF.SpecEValue MS.GF.EValue MS.GF.QValue
## 1               174     3.764831e-27 1.086033e-20            0
## 2               245     6.902626e-26 1.988774e-19            0
## 3               264     1.778789e-25 5.129649e-19            0
## 4               178     1.792541e-24 5.163566e-18            0
## 5               252     1.510364e-23 4.356914e-17            0
## 6               138     1.618941e-23 4.658952e-17            0
##   MS.GF.PepQValue
## 1               0
## 2               0
## 3               0
## 4               0
## 5               0
## 6               0

The mzID package, has similar functionality to parse identification files, and was the first one to provide such capabilities in R. The main difference with mzR is that is parses the files using the XMLpackage and reads the whole data into memory rather than relying on proteowizard, and is slower.

Exercise Data wrangling with identification data; the standard tidyverse tools are fit for purpose here. Extract and combine the PSMs and their scores as described above and combine them. From the available data, calculate the length of each peptide (you can use nchar with the peptide sequence sequence) and the number of peptides for each protein (DatabaseDescription). Plot the length of the proteins for their respective number of peptides. Optionally, stratify the plot by the peptide e-value score (MS.GF.EValue) using for example cut to define bins.

suppressPackageStartupMessages(library("dplyr"))

## Warning: package 'dplyr' was built under R version 3.6.0

iddf <- as_tibble(iddf) %>%
    mutate(peplen = nchar(as.character(sequence)))

## Warning: package 'bindrcpp' was built under R version 3.6.0

npeps <- iddf %>%
    group_by(DatabaseDescription) %>%
    tally
iddf <- full_join(iddf, npeps)

## Joining, by = "DatabaseDescription"

library("ggplot2")

## Warning: package 'ggplot2' was built under R version 3.6.0

ggplot(ids, aes(x = n, y = DBseqLength)) + geom_point()

## Error in ggplot(ids, aes(x = n, y = DBseqLength)): object 'ids' not found

ids$evalBins <- cut(ids$MS.GF.EValue, summary(ids$MS.GF.EValue))

## Error in cut(ids$MS.GF.EValue, summary(ids$MS.GF.EValue)): object 'ids' not found

ggplot(ids, aes(x = n, y = DBseqLength, color = peplen)) +
    geom_point() +
    facet_wrap(~ evalBins)

## Error in ggplot(ids, aes(x = n, y = DBseqLength, color = peplen)): object 'ids' not found

5.2 MS/MS database search

While searches are generally performed using third-party software independently of R or can be started from R using a system call, the MSGFplus package enables to perform a search using the MSGF+ engine, as illustrated below.

We search the TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML file against the fasta file from PXD000001 using MSGFplus.

We first download the fasta files from ProteomeXchange

fas <- pxget(px, "erwinia_carotovora.fasta")

## Downloading 1 file

basename(fas)

## [1] "erwinia_carotovora.fasta"

Below, we setup and run the search33 In the runMSGF call, the memory allocated to the java virtual machine is limited to 1GB. In general, there is no need to specify this argument, unless you experience an error regarding the maximum heap size..

library("MSGFplus")
msgfpar <- msgfPar(database = fas,
                   instrument = 'HighRes',
                   tda = TRUE,
                   enzyme = 'Trypsin',
                   protocol = 'iTRAQ')
idres <- runMSGF(msgfpar, mzf, memory=1000)

## '/usr/bin/java' -Xmx1000M -jar '/home/lg390/R/x86_64-pc-linux-gnu-library/3.4/MSGFplus/MSGFPlus/MSGFPlus.jar' -s '/home/lg390/Documents/Teaching/bioc-ms-prot/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML' -o '/home/lg390/Documents/Teaching/bioc-ms-prot/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid' -d '/home/lg390/Documents/Teaching/bioc-ms-prot/erwinia_carotovora.fasta' -tda 1 -inst 1 -e 1 -protocol 2 
## 
## reading TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid... DONE!

idres

## An mzID object
## 
## Software used:   MS-GF+ (version: Beta (v10072))
## 
## Rawfile:         /home/lg390/Documents/Teaching/bioc-ms-prot/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML
## 
## Database:        /home/lg390/Documents/Teaching/bioc-ms-prot/erwinia_carotovora.fasta
## 
## Number of scans: 5343
## Number of PSM's: 5656

## identification file (needed below)
basename(mzID::files(idres)$id)

## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"

A graphical interface to perform the search the data and explore the results is also available:

library("MSGFgui")
MSGFgui()

The MSGFgui interface

The rTANDEM package can be used to perform a search with XTandem software.

5.3 Analysing search results

The MSnID package can be used for post-search filtering of MS/MS identifications. One starts with the construction of an MSnID object that is populated with identification results that can be imported from a data.frame or from mzIdenML files. Here, we will use the example identification data provided with the package.

mzids <- system.file("extdata", "c_elegans.mzid.gz", package="MSnID")
basename(mzids)

## [1] "c_elegans.mzid.gz"

We start by loading the package, initialising the MSnID object, and add the identification result from our mzid file (there could of course be more that one).

library("MSnID")
msnid <- MSnID(".")

## Note, the anticipated/suggested columns in the
## peptide-to-spectrum matching results are:
## -----------------------------------------------
## accession
## calculatedMassToCharge
## chargeState
## experimentalMassToCharge
## isDecoy
## peptide
## spectrumFile
## spectrumID

msnid <- read_mzIDs(msnid, mzids)

## Loaded cached data

show(msnid)

## MSnID object
## Working directory: "."
## #Spectrum Files:  1 
## #PSMs: 12263 at 36 % FDR
## #peptides: 9489 at 44 % FDR
## #accessions: 7414 at 76 % FDR

Printing the MSnID object returns some basic information such as

• Working directory.
• Number of spectrum files used to generate data.
• Number of peptide-to-spectrum matches and corresponding FDR.
• Number of unique peptide sequences and corresponding FDR.
• Number of unique proteins or amino acid sequence accessions and corresponding FDR.

The package then enables to define, optimise and apply filtering based for example on missed cleavages, identification scores, precursor mass errors, etc. and assess PSM, peptide and protein FDR levels. To properly function, it expects to have access to the following data

## [1] "accession"                "calculatedMassToCharge"  
## [3] "chargeState"              "experimentalMassToCharge"
## [5] "isDecoy"                  "peptide"                 
## [7] "spectrumFile"             "spectrumID"

which are indeed present in our data:

names(msnid)

##  [1] "spectrumID"                "scan number(s)"           
##  [3] "acquisitionNum"            "passThreshold"            
##  [5] "rank"                      "calculatedMassToCharge"   
##  [7] "experimentalMassToCharge"  "chargeState"              
##  [9] "MS-GF:DeNovoScore"         "MS-GF:EValue"             
## [11] "MS-GF:PepQValue"           "MS-GF:QValue"             
## [13] "MS-GF:RawScore"            "MS-GF:SpecEValue"         
## [15] "AssumedDissociationMethod" "IsotopeError"             
## [17] "isDecoy"                   "post"                     
## [19] "pre"                       "end"                      
## [21] "start"                     "accession"                
## [23] "length"                    "description"              
## [25] "pepSeq"                    "modified"                 
## [27] "modification"              "idFile"                   
## [29] "spectrumFile"              "databaseFile"             
## [31] "peptide"

Here, we summarise a few steps and redirect the reader to the package’s vignette for more details:

5.3.1 Analysis of peptide sequences

Cleaning irregular cleavages at the termini of the peptides and missing cleavage site within the peptide sequences. The following two function call create the new numMisCleavages and numIrrCleabages columns in the MSnID object

msnid <- assess_termini(msnid, validCleavagePattern="[KR]\\.[^P]")
msnid <- assess_missed_cleavages(msnid, missedCleavagePattern="[KR](?=[^P$])")

5.3.2 Trimming the data

Now, we can use the apply_filter function to effectively apply filters. The strings passed to the function represent expressions that will be evaluated, this keeping only PSMs that have 0 irregular cleavages and 2 or less missed cleavages.

msnid <- apply_filter(msnid, "numIrregCleavages == 0")
msnid <- apply_filter(msnid, "numMissCleavages <= 2")
show(msnid)

## MSnID object
## Working directory: "."
## #Spectrum Files:  1 
## #PSMs: 7838 at 17 % FDR
## #peptides: 5598 at 23 % FDR
## #accessions: 3759 at 53 % FDR

5.3.3 Parent ion mass errors

Using "calculatedMassToCharge" and "experimentalMassToCharge", the mass_measurement_error function calculates the parent ion mass measurement error in parts per million.

summary(mass_measurement_error(msnid))

##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -2184.0640    -0.6992     0.0000    17.6146     0.7512  2012.5178

We then filter any matches that do not fit the +/- 20 ppm tolerance

msnid <- apply_filter(msnid, "abs(mass_measurement_error(msnid)) < 20")
summary(mass_measurement_error(msnid))

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -19.7797  -0.5866   0.0000  -0.2970   0.5713  19.6758

5.3.4 Filtering criteria

Filtering of the identification data will rely on

• -log10 transformed MS-GF+ Spectrum E-value, reflecting the goodness of match experimental and theoretical fragmentation patterns

msnid$msmsScore <- -log10(msnid$`MS-GF:SpecEValue`)

• the absolute mass measurement error (in ppm units) of the parent ion

msnid$absParentMassErrorPPM <- abs(mass_measurement_error(msnid))

MS2 filters are handled by a special MSnIDFilter class objects, where individual filters are set by name (that is present in names(msnid)) and comparison operator (>, <, = , …) defining if we should retain hits with higher or lower given the threshold and finally the threshold value itself.

filtObj <- MSnIDFilter(msnid)
filtObj$absParentMassErrorPPM <- list(comparison="<", threshold=10.0)
filtObj$msmsScore <- list(comparison=">", threshold=10.0)
show(filtObj)

## MSnIDFilter object
## (absParentMassErrorPPM < 10) & (msmsScore > 10)

We can then evaluate the filter on the identification data object, which return the false discovery rate and number of retained identifications for the filtering criteria at hand.

evaluate_filter(msnid, filtObj)

##           fdr    n
## PSM         0 3807
## peptide     0 2455
## accession   0 1009

5.3.5 Filter optimisation

Rather than setting filtering values by hand, as shown above, these can be set automativally to meet a specific false discovery rate.

filtObj.grid <- optimize_filter(filtObj, msnid, fdr.max=0.01,
                                method="Grid", level="peptide",
                                n.iter=500)
show(filtObj.grid)

## MSnIDFilter object
## (absParentMassErrorPPM < 3) & (msmsScore > 7.4)

evaluate_filter(msnid, filtObj.grid)

##                   fdr    n
## PSM       0.004097561 5146
## peptide   0.006447651 3278
## accession 0.021996616 1208

Filters can eventually be applied (rather than just evaluated) using the apply_filter function.

msnid <- apply_filter(msnid, filtObj.grid)
show(msnid)

## MSnID object
## Working directory: "."
## #Spectrum Files:  1 
## #PSMs: 5146 at 0.41 % FDR
## #peptides: 3278 at 0.64 % FDR
## #accessions: 1208 at 2.2 % FDR

And finally, identifications that matched decoy and contaminant protein sequences are removed

msnid <- apply_filter(msnid, "isDecoy == FALSE")
msnid <- apply_filter(msnid, "!grepl('Contaminant',accession)")
show(msnid)

## MSnID object
## Working directory: "."
## #Spectrum Files:  1 
## #PSMs: 5117 at 0 % FDR
## #peptides: 3251 at 0 % FDR
## #accessions: 1179 at 0 % FDR

The resulting filtered identification data can be exported to a data.frame or to a dedicated MSnSet data structure for quantitative MS data, described below, and further processed and analyses using appropriate statistical tests.

6 Visualisation

6.1 Visualising raw data

The importance of flexible access to specialised data becomes visible in the figure below44 Figure and code taken from the RforProteomics visualisation vignette. Not only can we access specific data and understand/visualise them, but we can transverse all the data and extracted/visualise/understand structured slices of data.

Visual summary of a full round of MS1 and MS2 acquisition for specific MS1 spectrum.

In this code chunks we start by selecting relevant spectra of interest. We will focus on the first MS1 spectrum acquired after 30 minutes of retention time.

## (1) Open raw data file
ms <- openMSfile(mzf)
## (2) Extract the header information
hd <- header(ms)
## (3) MS1 spectra indices
ms1 <- which(hd$msLevel == 1)
## (4) Select MS1 spectra with retention time between 30 and 35 minutes
rtsel <- hd$retentionTime[ms1] / 60 > 30 & hd$retentionTime[ms1] / 60 < 35
## (5) Indices of the 1st and 2nd MS1 spectra after 30 minutes
i <- ms1[which(rtsel)][1]
j <- ms1[which(rtsel)][2]
## (6) Interleaved MS2 spectra
ms2 <- (i+1):(j-1)

Now now extract and plot all relevant information:

1. The upper panel represents the chromatogram of the /home/lg390/Documents/Teaching/bioc-ms-prot/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML raw data file, produced by plotting the data returned by the chromatogram function.

plot(chromatogram(ms)[[1]], type = "l")

A chromatogram.

2. We concentrate at a specific retention time, 30:1 minutes (1800.6836 seconds)

plot(chromatogram(ms)[[1]], type = "l")
abline(v = hd[i, "retentionTime"]/60, col = "red")

A chromatogram with highlighted retention time of interest.

3. This corresponds to the 2807th MS1 spectrum, shown on the second row of figures.

plot(peaks(ms, i), type = "l", xlim = c(400, 1000))
legend("topright", bty = "n",
       legend = paste0(
           "Acquisition ", hd[i, "acquisitionNum"],  "\n",
           "Retention time ", formatRt(hd[i, "retentionTime"])))

The MS1 spectrum at the retention time of interest.

4. The ions that were selected for MS2 are highlighted by vertical lines. These are represented in the bottom part of the figure.

plot(peaks(ms, i), type = "l", xlim = c(400, 1000))
legend("topright", bty = "n",
       legend = paste0(
           "Acquisition ", hd[i, "acquisitionNum"],  "\n",
           "Retention time ", formatRt(hd[i, "retentionTime"])))
abline(v = hd[ms2, "precursorMZ"],
       col = c("#FF000080",
           rep("#12121280", 9)))

The MS1 spectrum at the retention time of interest, highlighting the peptides selected for MS2.

5. On the right, we zoom on the isotopic envelope of one peptide in particular (the one highlighted with a red line).

plot(peaks(ms, i), type = "l", xlim = c(521, 522.5))
abline(v = hd[ms2, "precursorMZ"], col = "#FF000080")

Isotopic envelope of a peptide.

6. A final loop through the relevant MS2 spectra plots the length(ms2) MS2 spectra highlighted above.

par(mfrow = c(5, 2), mar = c(2, 2, 0, 1))
for (ii in ms2) {
    p <- peaks(ms, ii)
    plot(p, xlab = "", ylab = "", type = "h", cex.axis = .6)
    legend("topright", legend = paste0("Prec M/Z\n",
                           round(hd[ii, "precursorMZ"], 2)),
           bty = "n", cex = .8)
}

MS2 spectra of all selected precursors from the previous MS1 spectrum.

Putting it all together:

layout(lout)
par(mar=c(4,2,1,1))

plot(chromatogram(ms)[[1]], type = "l")
abline(v = hd[i, "retentionTime"]/60, col = "red")
par(mar = c(3, 2, 1, 0))
plot(peaks(ms, i), type = "l", xlim = c(400, 1000))
legend("topright", bty = "n",
       legend = paste0(
           "Acquisition ", hd[i, "acquisitionNum"],  "\n",
           "Retention time ", formatRt(hd[i, "retentionTime"])))
abline(h = 0)
abline(v = hd[ms2, "precursorMZ"],
       col = c("#FF000080",
           rep("#12121280", 9)))

par(mar = c(3, 0.5, 1, 1))
plot(peaks(ms, i), type = "l", xlim = c(521, 522.5),
     yaxt = "n")
abline(h = 0)
abline(v = hd[ms2, "precursorMZ"], col = "#FF000080")

par(mar = c(2, 2, 0, 1))
for (ii in ms2) {
    p <- peaks(ms, ii)
    plot(p, xlab = "", ylab = "", type = "h", cex.axis = .6)
    legend("topright", legend = paste0("Prec M/Z\n",
                           round(hd[ii, "precursorMZ"], 2)),
           bty = "n", cex = .8)
}

Visual summary of a full round of MS1 and MS2 acquisition for specific MS1 spectrum.

Below, we illustrate some additional visualisation and animations of raw MS data, also taken from the RforProteomics visualisation vignette. On the left, we have a heatmap visualisation of a MS map and a 3 dimensional representation of the same data. On the right, 2 MS1 spectra in blue and the set of interleaves 10 MS2 spectra.

## (1) MS space heaptmap
M <- MSmap(ms, ms1[rtsel], 521, 523, .005, hd)

## 1

ff <- colorRampPalette(c("yellow", "steelblue"))
trellis.par.set(regions=list(col=ff(100)))
m1 <- plot(M, aspect = 1, allTicks = FALSE)
## (2) Same data as (1), in 3 dimenstion
M@map[msMap(M) == 0] <- NA
m2 <- plot3D(M, rgl = FALSE)
## (3) The 2 MS1 and 10 interleaved MS2 spectra from above
i <- ms1[which(rtsel)][1]
j <- ms1[which(rtsel)][2]
M2 <- MSmap(ms, i:j, 100, 1000, 1, hd)

## 1

m3 <- plot3D(M2)
grid.arrange(m1, m2, m3, ncol = 3)

Plotting MS maps along retention time, MZ range and intensity.

Below, we have animations build from extracting successive slices as above.

6.2 Visualising identification data

Annotated spectra and comparing spectra.

par(mfrow = c(1, 2))
itraqdata2 <- pickPeaks(itraqdata, verbose = FALSE) ## centroiding
s <- "SIGFEGDSIGR"
plot(itraqdata2[[14]], s, main = s)
plot(itraqdata2[[25]], itraqdata2[[28]], sequences = rep("IMIDLDGTENK", 2))

Annotating and comparing MS2 spectra.

The annotation of spectra is obtained by simulating fragmentation of a peptide and matching observed peaks to fragments:

calculateFragments("SIGFEGDSIGR")

##            mz  ion type pos z        seq
## 1    88.03931   b1    b   1 1          S
## 2   201.12337   b2    b   2 1         SI
## 3   258.14483   b3    b   3 1        SIG
## 4   405.21324   b4    b   4 1       SIGF
## 5   534.25583   b5    b   5 1      SIGFE
## 6   591.27729   b6    b   6 1     SIGFEG
## 7   706.30423   b7    b   7 1    SIGFEGD
## 8   793.33626   b8    b   8 1   SIGFEGDS
## 9   906.42032   b9    b   9 1  SIGFEGDSI
## 10  963.44178  b10    b  10 1 SIGFEGDSIG
## 11  175.11895   y1    y   1 1          R
## 12  232.14041   y2    y   2 1         GR
## 13  345.22447   y3    y   3 1        IGR
## 14  432.25650   y4    y   4 1       SIGR
## 15  547.28344   y5    y   5 1      DSIGR
## 16  604.30490   y6    y   6 1     GDSIGR
##  [ reached getOption("max.print") -- omitted 16 rows ]

Visualising a pair of spectra means that we can access them, and that, in addition to plotting, we can manipulate them and perform computations. The two spectra corresponding to the IMIDLDGTENK peptide, for example have 22 common peaks, a correlation of 0.198 and a dot product of 0.21 (see ?compareSpectra for details).

7 Quantitative proteomics

There are a wide range of proteomics quantitation techniques that can broadly be classified as labelled vs. label-free, depending whether the features are labelled prior the MS acquisition and the MS level at which quantitation is inferred, namely MS1 or MS2.

	Label-free	Labelled
MS1	XIC	SILAC, 15N
MS2	Counting	iTRAQ, TMT

In terms of raw data quantitation, most efforts have been devoted to MS2-level quantitation. Label-free XIC quantitation has however been addressed in the frame of metabolomics data processing by the xcms infrastructure.

Below is a list of suggested packages for some common proteomics quantitation technologies:

• Isobaric tagging (iTRAQ and TMT): MSnbase and isobar.
• Label-free: xcms (metabolomics).
• Counting: MSnbase and MSnID for peptide-spectrum matching confidence assessment.
• N14N15 for heavy Nitrogen-labelled data.

Isobaric tagging

An MSnExp is converted to an MSnSet by the quantitation method. Below, we use the iTRAQ 4-plex isobaric tagging strategy (defined by the iTRAQ4 parameter; other tags are available: see ?ReporterIons) and the max method to calculate the use the maximum of the reporter peak for quantitation.

plot(msexp[[1]], full=TRUE, reporters = iTRAQ4)

MS2 spectrum and it’s iTRAQ4 reporter ions.

msset <- quantify(msexp, method = "max", reporters = iTRAQ4)

The figure below give a schematics of an MSnSet instance and the relation between the assay data and the respective feature and sample metadata, accessible respectively with the exprs, fData and pData functions.

MSnSet structure

New columns can be added to the metadata slots.

exprs(msset)

##       iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
## F1.S1   706555.7   685055.1   929016.1   668245.2
## F1.S2   260663.7   212745.0   163782.8   239142.7
## F1.S3  2213566.0  2069209.6  2204032.2  2331846.8
## F1.S4   616043.4   705976.6   671828.8   666845.6
## F1.S5  1736128.2  1787622.5  1795311.8  1825523.0

pData(msset)

##                  mz reporters
## iTRAQ4.114 114.1112    iTRAQ4
## iTRAQ4.115 115.1083    iTRAQ4
## iTRAQ4.116 116.1116    iTRAQ4
## iTRAQ4.117 117.1150    iTRAQ4

pData(msset)$groups <- rep(c("Treat", "Cond"), each = 2)
pData(msset)

##                  mz reporters groups
## iTRAQ4.114 114.1112    iTRAQ4  Treat
## iTRAQ4.115 115.1083    iTRAQ4  Treat
## iTRAQ4.116 116.1116    iTRAQ4   Cond
## iTRAQ4.117 117.1150    iTRAQ4   Cond

Another useful slot is processingData, accessed with processingData(.), that records all the processing that objects have undergone since their creation.

processingData(msset)

## - - - Processing information - - -
## Data loaded: Tue Jul 10 15:18:02 2018 
## iTRAQ4 quantification by max: Tue Jul 10 15:18:30 2018 
##  MSnbase version: 2.6.1

See also The isobar package supports quantitation from centroided mgf peak lists or its own tab-separated files that can be generated from Mascot and Phenyx vendor files.

Spectral counting

Other MS2 quantitation methods available in quantify include the (normalised) spectral index SI and (normalised) spectral abundance factor SAF or simply a simple count method55 The code below is for illustration only - it doesn’t make much sense to perform any of these quantitations on such a multiplexed data.

exprs(si <- quantify(msexp, method = "SIn"))

##         dummyiTRAQ.mzXML
## ECA0510     0.0006553518
## ECA0984     0.0035384487
## ECA1028     0.0002684726

exprs(saf <- quantify(msexp, method = "NSAF"))

##         dummyiTRAQ.mzXML
## ECA0510        0.4306167
## ECA0984        0.3094475
## ECA1028        0.2599359

Note that spectra that have not been assigned any peptide (NA) or that match non-unique peptides (npsm > 1) are discarded in the counting process.

As shown above, the MSnID package enables to explore and assess the confidence of identification data using mzid files. A subset of all peptide-spectrum matches, that pass a specific false discovery rate threshold can them be converted to an MSnSet, where the number of peptide occurrences are used to populate the assay data.

8 Importing third-party quantitation data

The Proteomics Standard Initiative (PSI) mzTab file format is aimed at providing a simpler (than XML formats) and more accessible file format to the wider community. It is composed of a key-value metadata section and peptide/protein/small molecule tabular sections. These data can be imported with the readMzTabData function66 We specify version 0.9 (which generates the warning) to fit with the version of that file. For recent files, the version argument should be ignored to use the importer for the current file version 1.0..

mztf <- pxget(px, "F063721.dat-mztab.txt")

## Downloading 1 file

(mzt <- readMzTabData(mztf, what = "PEP", version = "0.9"))

## Warning: Version 0.9 is deprecated. Please see '?readMzTabData' and '?
## MzTab' for details.

## MSnSet (storageMode: lockedEnvironment)
## assayData: 1528 features, 6 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: sub[1] sub[2] ... sub[6] (6 total)
##   varLabels: abundance
##   varMetadata: labelDescription
## featureData
##   featureNames: 1 2 ... 1528 (1528 total)
##   fvarLabels: sequence accession ... uri (14 total)
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:  
## - - - Processing information - - -
## mzTab read: Mon Jun 19 23:10:32 2017 
##  MSnbase version: 2.3.6

It is also possible to import arbitrary spreadsheets as MSnSet objects into R with the readMSnSet2 function. The main 2 arguments of the function are (1) a text-based spreadsheet and (2) column names of indices that identify the quantitation data. The latter can be queried with the getEcols function.

csv <- dir(system.file ("extdata" , package = "pRolocdata"),
           full.names = TRUE, pattern = "pr800866n_si_004-rep1.csv")
getEcols(csv, split = ",")

##  [1] "\"Protein ID\""              "\"FBgn\""                   
##  [3] "\"Flybase Symbol\""          "\"No. peptide IDs\""        
##  [5] "\"Mascot score\""            "\"No. peptides quantified\""
##  [7] "\"area 114\""                "\"area 115\""               
##  [9] "\"area 116\""                "\"area 117\""               
## [11] "\"PLS-DA classification\""   "\"Peptide sequence\""       
## [13] "\"Precursor ion mass\""      "\"Precursor ion charge\""   
## [15] "\"pd.2013\""                 "\"pd.markers\""

ecols <- 7:10
res <- readMSnSet2(csv, ecols)
head(exprs(res))

##   area.114 area.115 area.116 area.117
## 1 0.379000 0.281000 0.225000 0.114000
## 2 0.420000 0.209667 0.206111 0.163889
## 3 0.187333 0.167333 0.169667 0.476000
## 4 0.247500 0.253000 0.320000 0.179000
## 5 0.216000 0.183000 0.342000 0.259000
## 6 0.072000 0.212333 0.573000 0.142667

head(fData(res))

##   Protein.ID        FBgn Flybase.Symbol No..peptide.IDs Mascot.score
## 1    CG10060 FBgn0001104    G-ialpha65A               3       179.86
## 2    CG10067 FBgn0000044         Act57B               5       222.40
## 3    CG10077 FBgn0035720        CG10077               5       219.65
## 4    CG10079 FBgn0003731           Egfr               2        86.39
## 5    CG10106 FBgn0029506        Tsp42Ee               1        52.10
## 6    CG10130 FBgn0010638      Sec61beta               2        79.90
##   No..peptides.quantified PLS.DA.classification Peptide.sequence
## 1                       1                    PM                 
## 2                       9                    PM                 
## 3                       3                                       
## 4                       2                    PM                 
## 5                       1                              GGVFDTIQK
## 6                       3              ER/Golgi                 
##   Precursor.ion.mass Precursor.ion.charge     pd.2013 pd.markers
## 1                                                  PM    unknown
## 2                                                  PM    unknown
## 3                                             unknown    unknown
## 4                                                  PM    unknown
## 5            626.887                    2 Phenotype 1    unknown
## 6                                            ER/Golgi         ER

9 Data processing and analysis

9.1 Raw data processing

For raw data processing look at MSnbase’s clean, smooth, pickPeaks, removePeaks and trimMz for MSnExp and spectra processing methods.

As an illustration, we show the pickPeaks function on the itraqdata data. Centoiding transforms the distribution of M/Z values measured for an ion (i.e. a set of M/Z and intensities, first figure below) into a single M/Z and intensity pair of values (second figure below).

library("ggplot2") ## for coord_cartesian
data(itraqdata)
plot(itraqdata[[10]], full = TRUE) +
    coord_cartesian(xlim = c(915, 925))

Peak picking: profile mode.

itraqdata2 <- pickPeaks(itraqdata)
plot(itraqdata2[[10]], full = TRUE, w1 = 0.05) +
    coord_cartesian(xlim = c(915, 925))

Peak picking: centroided.

The MALDIquant and xcms packages also features a wide range of raw data processing methods on their own ad hoc data instance types.

9.2 Processing and normalisation

Each different types of quantitative data will require their own pre-processing and normalisation steps. Both isobar and MSnbase allow to correct for isobaric tag impurities normalise the quantitative data.

data(itraqdata)
qnt <- quantify(itraqdata, method = "trap", reporters = iTRAQ4)
impurities <- matrix(c(0.929, 0.059, 0.002, 0.000,
                       0.020, 0.923, 0.056, 0.001,
                       0.000, 0.030, 0.924, 0.045,
                       0.000, 0.001, 0.040, 0.923),
                     nrow = 4, byrow = TRUE)
## or, using makeImpuritiesMatrix()
## impurities <- makeImpuritiesMatrix(4)
qnt <- purityCorrect(qnt, impurities)
processingData(qnt)

## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
## Updated from version 0.3.0 to 0.3.1 [Fri Jul  8 20:23:25 2016] 
## iTRAQ4 quantification by trapezoidation: Tue Jul 10 15:18:33 2018 
## Purity corrected: Tue Jul 10 15:18:33 2018 
##  MSnbase version: 1.1.22

Various normalisation methods can be applied the MSnSet instances using the normalise method: variance stabilisation (vsn), quantile (quantiles), median or mean centring (center.media or center.mean), …

qnt <- normalise(qnt, "quantiles")
processingData(qnt)

## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
## Updated from version 0.3.0 to 0.3.1 [Fri Jul  8 20:23:25 2016] 
## iTRAQ4 quantification by trapezoidation: Tue Jul 10 15:18:33 2018 
## Purity corrected: Tue Jul 10 15:18:33 2018 
## Normalised (quantiles): Tue Jul 10 15:18:33 2018 
##  MSnbase version: 1.1.22

The combineFeatures method combines spectra/peptides quantitation values into protein data. The grouping is defined by the groupBy parameter, which is generally taken from the feature metadata (protein accessions, for example).

gb <- fData(qnt)$ProteinDescription
prt <- combineFeatures(qnt, groupBy = gb, fun = "median")

## Your data contains missing values. Please read the relevant
## section in the combineFeatures manual page for details the effects
## of missing values on data aggregation.

processingData(prt)

## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
## Updated from version 0.3.0 to 0.3.1 [Fri Jul  8 20:23:25 2016] 
## iTRAQ4 quantification by trapezoidation: Tue Jul 10 15:18:33 2018 
## Purity corrected: Tue Jul 10 15:18:33 2018 
## Normalised (quantiles): Tue Jul 10 15:18:33 2018 
## Combined 55 features into 39 using median: Tue Jul 10 15:18:33 2018 
##  MSnbase version: 2.6.1

Missing values

Finally, proteomics data analysis is generally hampered by missing values. Missing data imputation is a sensitive operation whose success will be guided by many factors, such as degree and (non-)random nature of the missingness.

Below, we load an MSnSet with missing values, count the number missing and non-missing values.

data(naset)
table(is.na(naset))

## 
## FALSE  TRUE 
## 10254   770

The naplot figure will reorder cells within the data matrix so that the experiments and features with many missing values will be grouped towards the top and right of the heatmap, and barplots at the top and right summarise the number of missing values in the respective samples (column) and rows (rows).

naplot(naset)

Overview of missing values.

The importance of missing values in a dataset will depend on the quantitation technology employed. Label-free quantitation in particular can suffer from a very high number of missing values.

Missing value in MSnSet instances can be filtered out with the filterNA functions. By default, it removes features that contain at least NA value.

## remove features with missing values
tmp <- filterNA(naset)
processingData(tmp)

## - - - Processing information - - -
## Subset [689,16][301,16] Tue Jul 10 15:18:33 2018 
## Removed features with more than 0 NAs: Tue Jul 10 15:18:33 2018 
## Dropped featureData's levels Tue Jul 10 15:18:33 2018 
##  MSnbase version: 1.15.6

It is of course possible to impute missing values (?impute). This is however not a straightforward thing, as is likely to dramatically fail when a high proportion of data is missing (10s of %)77 Note that when using limma for instance, downstream analyses can handle missing values. Still, it is recommended to explore missingness as part of the exploratory data analysis.. But also, there are two types of mechanisms resulting in missing values in LC/MSMS experiments.

• Missing values resulting from absence of detection of a feature, despite ions being present at detectable concentrations. For example in the case of ion suppression or as a result from the stochastic, data-dependent nature of the MS acquisition method. These missing value are expected to be randomly distributed in the data and are defined as missing at random (MAR) or missing completely at random (MCAR).

• Biologically relevant missing values, resulting from the absence or the low abundance of ions (below the limit of detection of the instrument). These missing values are not expected to be randomly distributed in the data and are defined as missing not at random (MNAR).

Random and non-random missing values.

Different imputation methods are more appropriate to different classes of missing values (as documented in this paper). Values missing at random, and those missing not at random should be imputed with different methods.

Root-mean-square error (RMSE) observations standard deviation ratio (RSR), KNN and MinDet imputation. Lower (blue) is better. (See here for details)

Generally, it is recommended to use hot deck methods (nearest neighbour (left), maximum likelihood, …) when data are missing at random.Conversely, MNAR features should ideally be imputed with a left-censor (minimum value (right), but not zero, …) method.

## impute missing values using knn imputation
tmp <- impute(naset, method = "knn")

## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 12 rows with more than 50 % entries missing;
##  mean imputation used for these rows

processingData(tmp)

## - - - Processing information - - -
## Data imputation using knn Tue Jul 10 15:18:34 2018 
##   Using default parameters 
##  MSnbase version: 1.15.6

There are various methods to perform data imputation, as described in ?impute.

10 Statistical analysis

R in general and Bioconductor in particular are well suited for the statistical analysis of data of quantitative proteomics data. Several packages provide dedicated resources for proteomics data:

• MSstats: A set of tools for statistical relative protein significance analysis in Data dependent (DDA), SRM and Data independent acquisition (DIA) experiments. Data stored in data.frame or MSnSet objects can be used as input.

• msmsTests: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. All can be readily applied on MSnSet instances produced, for example by MSnID.

• isobar also provides dedicated infrastructure for the statistical analysis of isobaric data.

11 Machine learning

The MLInterfaces package provides a unified interface to a wide range of machine learning algorithms. Initially developed for microarray and ExpressionSet instances, the pRoloc package enables application of these algorithms to MSnSet data.

11.1 Dimensionality reduction

Dimensionality reduction is very frequently used to summarise high-dimensional data. Below we will use principal component analysis (PCA), but other methods can be applied. Below, we will use the plot2D function from the pRoloc package88 While originally developed for the analysis of spatial/organelle proteomics data in mind, it is applicable many use cases., that will extract the expression values in the assay data, perform dimensionality reduction, an produce the scatter plot.

Let’s first use plot2D to visualise the pattern in 20 protein quantitation values (initial 20 dimensional data). Here, we use an example from spatial proteomics, where the quantitative protein profiles reflect the proteins sub-cellular localisation (from Christoforou et al, 2016, see also Breckels et al, 2016 for more data analysis background). We will use the known localisation of some proteins (marker proteins) to annotate the plot (using the fcol argument).

library("pRoloc")
library("pRolocdata")
data(hyperLOPIT2015)
plot2D(hyperLOPIT2015, fcol = "markers")
addLegend(hyperLOPIT2015, fcol = "markers", cex = .7)

PCA plot for protein sub-cellular localisation.

In other cases, we want to visualise the relation of samples. plot2D uses the rows of the data to perform dimensionality reduction. To use the columns, we just need to transpose the MSnSet. By doing so, the pData becomes the fData and vice versa.

Let’s use a time-course experiment on stem cells (Mulvey et al. 2015). Below, we use the times (time points) variable to set colours and rep (replicate numbers) to set the plotting characters.

data(mulvey2015)
head(pData(mulvey2015))

##           rep times cond
## rep1_0hr    1     1    1
## rep1_16hr   1     2    1
## rep1_24hr   1     3    1
## rep1_48hr   1     4    1
## rep1_72hr   1     5    1
## rep1_XEN    1     6    1

plot2D(t(mulvey2015),  fcol = "times", fpch = "rep", cex = 2)
addLegend(t(mulvey2015),  fcol = "times")

## Classification

The example below uses knn with the 5 closest neighbours and the MLInterfaces package as an illustration to classify proteins of unknown sub-cellular localisation to one of 9 possible organelles.

library("MLInterfaces")
library("pRolocdata")
data(dunkley2006)
traininds <- which(fData(dunkley2006)$markers != "unknown")
ans <- MLearn(markers ~ ., data = t(dunkley2006), knnI(k = 5), traininds)
ans

## MLInterfaces classification output container
## The call was:
## MLearn(formula = markers ~ ., data = t(dunkley2006), .method = knnI(k = 5), 
##     trainInd = traininds)
## Predicted outcome distribution for test set:
## 
##      ER lumen   ER membrane         Golgi Mitochondrion       Plastid 
##             5           140            67            51            29 
##            PM      Ribosome           TGN       vacuole 
##            89            31             6            10 
## Summary of scores on test set (use testScores() method for details):
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.4000  1.0000  1.0000  0.9332  1.0000  1.0000

11.2 Clustering

A wide range of classification and clustering algorithms are also available, as described in the ?MLearn documentation page, used below.

kcl <- MLearn( ~ ., data = dunkley2006, kmeansI, centers = 12)
kcl

## clusteringOutput: partition table
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##  55  28  29  18  60  30  44 145  78 116  33  53 
## The call that created this object was:
## MLearn(formula = ~., data = dunkley2006, .method = kmeansI, centers = 12)

plot(kcl, exprs(dunkley2006))

Kmeans clustering using r Biocpkg('MLInterfaces') with an MSnSet object.

12 Annotation

All the Bioconductor annotation infrastructure, such as biomaRt, GO.db, organism specific annotations, .. are directly relevant to the analysis of proteomics data. A total of 191 ontologies, including some proteomics-centred annotations such as the PSI Mass Spectrometry Ontology, Molecular Interaction (PSI MI 2.5) or Protein Modifications are available through the rols

library("rols")
res <- OlsSearch(q = "ESI", ontology = "MS", exact = TRUE)

## Error: lexical error: invalid char in json text.
##                                        <!DOCTYPE html PUBLIC "-//W3C//
##                      (right here) ------^

res

## MSnSet (storageMode: lockedEnvironment)
## assayData: 888 features, 4 samples 
##   element names: exprs 
## protocolData: none
## phenoData: none
## featureData
##   featureNames: 1 2 ... 888 (888 total)
##   fvarLabels: Protein.ID FBgn ... pd.markers (12 total)
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:  
## - - - Processing information - - -
##  MSnbase version: 2.6.1

There is a single exact match (default is to retrieve 20 results), that can be retrieved and coerced to a Terms or data.frame object with

res <- olsSearch(res)

## Error in handle_url(handle, url, ...): no slot of name "url" for this object of class "MSnSet"

as(res, "Terms")

## Error in as(res, "Terms"): no method or default for coercing "MSnSet" to "Terms"

as(res, "data.frame")

##             X1       X2       X3     X4    X5       X6    X7     X8     X9
##             X10    X11    X12      X13    X14      X15     X16      X17
##            X18      X19   X20       X21    X22    X23    X24      X25
##              X26    X27   X28    X29   X30    X31   X32      X33   X34
##               X35    X36    X37      X38   X39    X40      X41   X42
##                X43       X44      X45   X46   X47     X48    X49    X50
##              X51   X52   X53    X54    X55   X56      X57    X58    X59
##            X60   X61    X62       X63   X64    X65      X66      X67
##               X68      X69      X70      X71      X72      X73     X74
##               X75    X76      X77    X78    X79   X80     X81    X82
##               X83      X84      X85    X86   X87      X88       X89
##               X90       X91   X92   X93    X94   X95    X96       X97
##             X98    X99  X100   X101    X102   X103    X104   X105     X106
##              X107     X108   X109  X110  X111      X112     X113    X114
##            X115  X116   X117  X118  X119     X120      X121   X122
##              X123      X124  X125    X126  X127     X128    X129  X130
##               X131  X132   X133     X134     X135  X136  X137  X138
##              X139     X140     X141     X142     X143  X144   X145  X146
##            X147    X148  X149     X150  X151    X152     X153    X154
##           X155     X156  X157   X158  X159     X160     X161      X162
##            X163      X164  X165  X166      X167   X168  X169  X170   X171
##             X172      X173     X174    X175  X176  X177   X178  X179  X180
##           X181   X182    X183  X184     X185   X186     X187   X188  X189
##             X190     X191   X192     X193  X194   X195  X196  X197   X198
##            X199  X200   X201  X202  X203  X204      X205    X206  X207
##            X208   X209   X210      X211      X212   X213    X214     X215
##             X216   X217      X218     X219     X220   X221     X222   X223
##            X224     X225  X226   X227  X228  X229   X230   X231      X232
##            X233      X234  X235     X236  X237   X238    X239  X240  X241
##            X242  X243     X244   X245  X246   X247     X248     X249
##             X250  X251  X252  X253     X254  X255  X256     X257     X258
##               X259  X260    X261  X262  X263    X264      X265      X266
##           X267   X268  X269      X270   X271  X272   X273  X274   X275
##            X276   X277   X278  X279   X280   X281   X282   X283  X284
##           X285  X286     X287     X288  X289      X290   X291     X292
##            X293   X294  X295    X296  X297     X298    X299    X300   X301
##              X302     X303  X304   X305  X306     X307     X308     X309
##            X310     X311    X312   X313     X314    X315   X316  X317
##              X318  X319     X320  X321     X322      X323     X324    X325
##            X326      X327     X328      X329   X330     X331     X332
##               X333   X334      X335     X336     X337   X338   X339
##             X340  X341     X342     X343     X344      X345     X346
##            X347  X348   X349     X350  X351  X352     X353   X354
##               X355  X356  X357    X358   X359  X360  X361   X362  X363
##            X364   X365  X366  X367  X368     X369  X370  X371    X372
##            X373      X374  X375     X376    X377  X378    X379   X380
##            X381  X382   X383  X384   X385    X386   X387   X388   X389
##              X390     X391  X392     X393   X394   X395     X396   X397
##              X398     X399  X400  X401     X402   X403     X404  X405
##            X406  X407     X408  X409  X410  X411  X412    X413     X414
##              X415   X416      X417      X418     X419   X420    X421
##              X422      X423     X424  X425      X426   X427   X428   X429
##              X430  X431    X432  X433   X434   X435  X436  X437  X438
##            X439   X440  X441      X442  X443      X444      X445  X446
##           X447    X448    X449     X450  X451     X452     X453   X454
##            X455      X456     X457   X458   X459   X460   X461  X462
##             X463  X464   X465  X466   X467     X468  X469    X470
##               X471  X472   X473   X474     X475     X476   X477  X478
##              X479   X480   X481   X482      X483  X484     X485      X486
##            X487  X488   X489   X490     X491      X492   X493     X494
##               X495   X496   X497   X498  X499   X500   X501   X502   X503
##             X504    X505  X506  X507  X508      X509     X510  X511
##             X512   X513     X514     X515  X516     X517  X518   X519
##              X520   X521   X522  X523      X524   X525  X526  X527
##              X528  X529  X530  X531   X532   X533   X534     X535   X536
##              X537   X538   X539    X540  X541      X542   X543  X544
##               X545    X546    X547     X548  X549  X550     X551     X552
##            X553    X554  X555      X556   X557      X558   X559  X560
##           X561  X562     X563  X564   X565  X566   X567     X568    X569
##            X570  X571   X572   X573   X574    X575   X576     X577
##              X578      X579  X580     X581    X582  X583      X584   X585
##           X586    X587  X588  X589  X590  X591     X592   X593  X594  X595
##           X596     X597   X598  X599     X600      X601     X602   X603
##             X604   X605   X606  X607     X608      X609   X610     X611
##            X612   X613   X614  X615      X616   X617  X618   X619   X620
##            X621   X622   X623  X624  X625  X626   X627   X628   X629
##            X630  X631   X632  X633     X634     X635    X636     X637
##            X638    X639  X640   X641  X642      X643     X644   X645
##            X646     X647    X648    X649  X650     X651  X652   X653  X654
##              X655   X656  X657     X658     X659  X660  X661  X662
##              X663     X664  X665  X666   X667     X668     X669     X670
##               X671  X672  X673   X674  X675  X676  X677  X678      X679
##            X680      X681     X682      X683     X684     X685     X686
##              X687     X688    X689     X690      X691   X692   X693   X694
##           X695     X696  X697   X698     X699   X700   X701      X702
##           X703  X704     X705     X706   X707  X708  X709   X710     X711
##           X712    X713     X714     X715     X716     X717   X718   X719
##              X720  X721  X722     X723  X724   X725     X726  X727   X728
##            X729  X730    X731  X732  X733  X734     X735  X736     X737
##              X738   X739   X740  X741   X742     X743  X744     X745
##             X746   X747     X748  X749     X750  X751      X752    X753
##            X754     X755    X756  X757  X758   X759  X760    X761    X762
##           X763  X764     X765   X766   X767     X768      X769  X770
##            X771   X772     X773    X774     X775  X776     X777  X778
##               X779  X780  X781    X782  X783     X784     X785   X786
##            X787   X788    X789      X790      X791     X792     X793  X794
##           X795    X796     X797   X798   X799   X800     X801     X802
##            X803  X804   X805     X806   X807   X808     X809  X810
##              X811     X812     X813    X814      X815     X816     X817
##              X818   X819  X820      X821  X822   X823   X824  X825   X826
##            X827    X828     X829  X830    X831     X832  X833     X834
##              X835   X836     X837   X838  X839  X840      X841    X842
##            X843   X844  X845     X846     X847     X848  X849     X850
##           X851     X852   X853     X854      X855    X856    X857  X858
##            X859  X860  X861   X862      X863     X864  X865  X866
##               X867    X868   X869     X870   X871     X872  X873  X874
##           X875     X876   X877     X878  X879   X880   X881   X882   X883
##               X884   X885   X886  X887  X888
##  [ reached getOption("max.print") -- omitted 4 rows ]

Data from the Human Protein Atlas is available via the hpar package.

13 More information

13.1 References and resources

• Visualisation of proteomics data using R and Bioconductor
• Using R and Bioconductor for proteomics data analysis
• RforProteomics: http://bioconductor.org/packages/RforProteomics

13.2 Other relevant packages/pipelines

• Analysis of post translational modification with isobar.
• Processing and analysis or isobaric tagging mass spectrometry with isobar and MSnbase.
• Analysis of spatial proteomics data and, more generally, supervised, semi-supervised learning, and transfer learning using the pRoloc package.
• Analysis of MALDI data with the MALDIquant package.
• Access to the Proteomics Standard Initiative Common QUery InterfaCe with the PSICQUIC package.
• Cardinal: A mass spectrometry imaging toolbox for statistical analysis.
• MSnID: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications.
• protViz: Visualising and Analysing Mass Spectrometry Related Data in Proteomics
• aLFQ: Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data.
• protiq: Protein (identification and) quantification based on peptide evidence.
• MSstats: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments

Data Independent Acquisition

• Analysis of label-free data from a Synapt G2 (including ion mobility) with synapter.
• SWATH2stats: Transform and Filter SWATH Data for Statistical Packages and
• specL: Prepare Peptide Spectrum Matches for Use in Targeted Proteomics
• SwathXtend: SWATH extended library generation and statistical data analysis

14 Session Information

sessionInfo()

## R version 3.5.0 Patched (2018-05-14 r74725)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.5 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.0
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] ggplot2_3.0.0         bindrcpp_0.2.2        dplyr_0.7.6          
##  [4] msdata_0.20.0         AnnotationHub_2.12.0  knitr_1.20           
##  [7] hpar_1.22.2           rols_2.8.1            MSGFplus_1.14.0      
## [10] pRolocdata_1.18.0     rpx_1.16.0            MSnID_1.14.0         
## [13] mzID_1.18.0           pRoloc_1.20.1         MLInterfaces_1.60.1  
## [16] cluster_2.0.7-1       annotate_1.58.0       XML_3.98-1.11        
## [19] AnnotationDbi_1.42.1  IRanges_2.14.10       S4Vectors_0.18.3     
## [22] gridExtra_2.3         lattice_0.20-35       RforProteomics_1.18.1
## [25] BiocInstaller_1.30.0  MSnbase_2.6.1         ProtGenerics_1.12.0  
## [28] BiocParallel_1.14.2   mzR_2.14.0            Rcpp_0.12.17         
## [31] Biobase_2.40.0        BiocGenerics_0.26.0   BiocStyle_2.8.2      
## 
## loaded via a namespace (and not attached):
##   [1] R.utils_2.6.0                 RUnit_0.4.32                 
##   [3] tidyselect_0.2.4              RSQLite_2.1.1                
##   [5] htmlwidgets_1.2               grid_3.5.0                   
##   [7] trimcluster_0.1-2             lpSolve_5.6.13               
##   [9] rda_1.0.2-2                   munsell_0.5.0                
##  [11] codetools_0.2-15              preprocessCore_1.42.0        
##  [13] DT_0.4                        withr_2.1.2                  
##  [15] colorspace_1.3-2              Category_2.46.0              
##  [17] highr_0.7                     geometry_0.3-6               
##  [19] robustbase_0.93-1             dimRed_0.1.0                 
##  [21] labeling_0.3                  mnormt_1.5-5                 
##  [23] hwriter_1.3.2                 bit64_0.9-7                  
##  [25] ggvis_0.4.3                   rprojroot_1.3-2              
##  [27] ipred_0.9-6                   randomForest_4.6-14          
##  [29] diptest_0.75-7                R6_2.2.2                     
##  [31] doParallel_1.0.11             gridSVG_1.6-0                
##  [33] flexmix_2.3-14                DRR_0.0.3                    
##  [35] bitops_1.0-6                  assertthat_0.2.0             
##  [37] promises_1.0.1                scales_0.5.0                 
##  [39] nnet_7.3-12                   gtable_0.2.0                 
##  [41] affy_1.58.0                   biocViews_1.48.2             
##  [43] ddalpha_1.3.4                 timeDate_3043.102            
##  [45] rlang_0.2.1                   CVST_0.2-2                   
##  [47] genefilter_1.62.0             RcppRoll_0.3.0               
##  [49] splines_3.5.0                 lazyeval_0.2.1               
##  [51] ModelMetrics_1.1.0            impute_1.54.0                
##  [53] hexbin_1.27.2                 broom_0.4.5                  
##  [55] yaml_2.1.19                   reshape2_1.4.3               
##  [57] abind_1.4-5                   threejs_0.3.1                
##  [59] crosstalk_1.0.0               backports_1.1.2              
##  [61] httpuv_1.4.4.2                RBGL_1.56.0                  
##  [63] caret_6.0-80                  tools_3.5.0                  
##  [65] lava_1.6.2                    psych_1.8.4                  
##  [67] gplots_3.0.1                  affyio_1.50.0                
##  [69] RColorBrewer_1.1-2            proxy_0.4-22                 
##  [71] plyr_1.8.4                    base64enc_0.1-3              
##  [73] progress_1.2.0                zlibbioc_1.26.0              
##  [75] purrr_0.2.5                   RCurl_1.95-4.10              
##  [77] prettyunits_1.0.2             rpart_4.1-13                 
##  [79] viridis_0.5.1                 sampling_2.8                 
##  [81] sfsmisc_1.1-2                 magrittr_1.5                 
##  [83] data.table_1.11.4             pcaMethods_1.72.0            
##  [85] mvtnorm_1.0-8                 whisker_0.3-2                
##  [87] R.cache_0.13.0                hms_0.4.2                    
##  [89] mime_0.5                      evaluate_0.10.1              
##  [91] xtable_1.8-2                  mclust_5.4.1                 
##  [93] compiler_3.5.0                biomaRt_2.36.1               
##  [95] tibble_1.4.2                  KernSmooth_2.23-15           
##  [97] crayon_1.3.4                  R.oo_1.22.0                  
##  [99] htmltools_0.3.6               later_0.7.3                  
## [101] tidyr_0.8.1                   lubridate_1.7.4              
## [103] DBI_1.0.0                     magic_1.5-8                  
## [105] MASS_7.3-50                   fpc_2.1-11                   
## [107] Matrix_1.2-14                 vsn_3.48.1                   
## [109] R.methodsS3_1.7.1             gdata_2.18.0                 
## [111] mlbench_2.1-1                 bindr_0.1.1                  
## [113] gower_0.1.2                   igraph_1.2.1                 
## [115] pkgconfig_2.0.1               foreign_0.8-70               
## [117] recipes_0.1.3                 MALDIquant_1.18              
## [119] xml2_1.2.0                    foreach_1.4.4                
## [121] prodlim_2018.04.18            stringr_1.3.1                
## [123] digest_0.6.15                 pls_2.6-0                    
## [125] graph_1.58.0                  rmarkdown_1.10               
## [127] dendextend_1.8.0              GSEABase_1.42.0              
## [129] curl_3.2                      kernlab_0.9-26               
## [131] shiny_1.1.0                   gtools_3.8.1                 
## [133] modeltools_0.2-21             nlme_3.1-137                 
## [135] jsonlite_1.5                  interactiveDisplay_1.18.0    
## [137] viridisLite_0.3.0             limma_3.36.2                 
## [139] pillar_1.2.3                  httr_1.3.1                   
## [141] DEoptimR_1.0-8                survival_2.42-4              
## [143] interactiveDisplayBase_1.18.0 glue_1.2.0                   
## [145] FNN_1.1                       gbm_2.1.3                    
## [147] prabclus_2.2-6                iterators_1.0.9              
## [149] bit_1.1-14                    class_7.3-14                 
## [151] stringi_1.2.3                 blob_1.1.1                   
## [153] caTools_1.17.1                memoise_1.1.0                
## [155] e1071_1.6-8